Regression Overdispersion?
A third, and often preferable, way is to add an observation-level random effect: library(lme4) data1$obs <- factor(seq_len(nrow(data1))) model <- glmer(y ~ x1 + x2 + (1 | obs), family=poisson(link=log), data=data1) See http://glmm.wikidot.com/faq and search for "individual-level random effects". Cheers, Rune
On 1 February 2015 at 19:55, David Barron <dnbarron at gmail.com> wrote:
There are two straightforward ways of modelling overdispersion: 1) Use glm as in your example but specify family=quasipoisson. 2) Use glm.nb in the MASS package, which fits a negative binomial model. On 1 February 2015 at 16:26, JvanDyne <e283851 at trbvm.com> wrote:
I am trying to use Poisson regression to model count data with four explanatory variables: ratio, ordinal, nominal and dichotomous ? x1, x2, x3 and x4. After playing around with the input for a bit, I have formed ? what I believe is ? a series of badly fitting models probably due to overdispersion [1] - e.g. model=glm(y ~ x1 + x2,family=poisson(link=log),data=data1) - and I was looking for some general guidance/direction/help/approach to correcting this in R. [1] ? I believe this as a. it?s, as I?m sure you?re aware, a possible reason for poor model fits; b.the following: tapply(data1$y,data$x2,function(x)c(mean=mean(x),variance=var(x))) seems to suggest that, whilst variance does appear to be some function of the mean, there is a consistently large difference between the two -- View this message in context: http://r.789695.n4.nabble.com/Regression-Overdispersion-tp4702611.html Sent from the R help mailing list archive at Nabble.com.
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